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Slowing the spread of Covid-19

An air of uncertainty descended on MIT’s campus in early March. Whispers and rumors about campus closing down swirled in the hallways. Students convened en masse on Killian Court to dance, hug, and cry as they were told they had until the end of the week to vacate campus. Within days, the Infinite Corridor’s usual stream of activity and noise was silenced.

While MIT’s dorms and classrooms became unnervingly quiet, there was a thrum of activity among faculty and researchers. Research teams across the Institute quickly swung into action, hatching plans and developing technologies to slow or stop the spread of the virus. These teams were among the only people allowed on campus this spring to work on Covid-19 related research.

The unprecedented nature of this global pandemic necessitates a diverse range of solutions. From designing low-cost ventilators to understanding how the virus is transmitted and manufacturing PPE, mechanical engineers have been a driving force in many research projects that seek to slow Covid-19’s spread and save lives.

“Mechanical engineers are used to developing concrete solutions for the grand challenges the world faces across a vast range of research areas,” says Evelyn Wang, Gail E. Kendall Professor and head of MIT’s Department of Mechanical Engineering. “This uniquely positioned our research community to serve as leaders in the global response to the Covid-19 pandemic.”

Since the beginning of the year, a number of mechanical engineering faculty and research staff at MIT have led collaborative research efforts in the fight against the virus. These projects have had a tangible impact — deepening our understanding of how the virus spreads, informing international guidelines, and protecting front-line workers and vulnerable populations.

Predicting the spread with machine learning

Earlier this year, as coronavirus cases spiked in countries like Italy, South Korea, and the United States, two main questions emerged: How many cases would there be in each country and what measures could be taken to stop the spread? George Barbastathis, professor of mechanical engineering, worked with Raj Dandekar, a PhD candidate studying civil and environmental engineering, to develop a model that could answer these questions.

The pair created the first-ever model that combined data from the spread of Covid-19 with a neural network to make predictions about the spread and determine which quarantine measures were effective. Dandekar first began developing the model as a project for MIT course 2.168 (Learning Machines), which Barbastathis teaches. He was inspired by a mathematical approach developed by Christopher Rackauckas, instructor of mathematics at MIT, that was published on a pre-print server in January of this year.

“I found it really


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